Table 3 |
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|
Example of calculations from data in Baker and Lindeman [Reference 9] |
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|
hospital |
before" group data |
after group data |
estimate |
std error |
weight |
||||
|
|
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|
n1 |
e1 |
P1 |
n2 |
e2 |
p2 |
y |
s |
w |
|
|
|
|||||||||
|
1 |
116 |
.586 |
.172 |
103 |
.223 |
.184 |
-.033 |
.143 |
44 |
|
2 |
180 |
.290 |
.080 |
180 |
.440 |
.090 |
.067 |
.196 |
24 |
|
3 |
373 |
.131 |
.110 |
421 |
.587 |
.100 |
-.022 |
.048 |
208 |
|
4 |
1000 |
.100 |
.040 |
1000 |
.450 |
.050 |
.029 |
.026 |
313 |
|
5 |
1298 |
.000 |
.074 |
1084 |
.480 |
.065 |
-.019 |
.022 |
333 |
|
6 |
1919 |
.000 |
.275 |
2073 |
.316 |
.229 |
-.146 |
.044 |
225 |
|
7 |
3195 |
.010 |
.030 |
3733 |
.290 |
.031 |
.004 |
.015 |
365 |
|
8 |
4778 |
.008 |
.194 |
4859 |
.586 |
.190 |
-.006 |
.014 |
369 |
|
9 |
4685 |
.187 |
.149 |
6170 |
.551 |
.125 |
-.046 |
.015 |
352 |
|
10 |
8108 |
.467 |
.248 |
9918 |
.678 |
.280 |
.152 |
.031 |
288 |
|
11 |
11159 |
.328 |
.209 |
11869 |
.499 |
.209 |
.000 |
.031 |
288 |
|
|
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|
n1 (n2) = number of subjects in "before" ("after") group. el (e2) = fraction of subjects in "before" ("after") group that had epdiural analgesia, p1 (p2) = fraction of subjects in "before" ("after") group that had a Cesarean section, y= estimated effect of epidural analgesia on the probability of Cesarean section = (p2-p1)/(e2-e1), s= standard error of y= square root of (p2 (1-p2))/n2 + p1 (1-p1)/n1) /(e2-e1)2, w* = weight used in random effects meta-analysis. We computed the weights as follows. Let i index studies, so yi and si are the values of y and s for study i. It is convenient to define w1 = I/ si2. Following DerSimonian and Laird [Reference 19], to compute v, the variance of the true effect among the k studies, we set v equal to the larger of (Q-(k-1)) / (Σwi - Σwi2/Σwi) and 0, where Q = Σwi (yi - m)2, m = Σyi wi/Σwi. The random-effects weights are w*i= 1/(si2 + v), and the summary statistic is y* = Σyi w*i/Σw*i, with standard error s* = square root of 1/Σw*i. Following Proschan and Follman [reference 20], the 95% confidence interval is (y* - tk- s*, y*+ tk-1 s*), where tk-1 is the value of the 97 ½ percentile of a t-distribution with k-1 degrees of freedom. In this example, k = 11, Q = 50.1, v = .0025, m =-.007, s* = .019 y* = -.005, t10 = 2.23, y* = -.005 and the 95% confidence interval is (-.047, .037). |
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|
Baker et al. BMC Medical Research Methodology 2001 1:9 doi:10.1186/1471-2288-1-9 |
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